code: def convert_to_coco_api(ds):
      coco_ds = COCO()
      # annotation IDs need to start at 1, not 0, see torchvision issue #1530
      ann_id = 1
      dataset = {"images": [], "categories": [], "annotations": []}
      categories = set()
      for img_idx in range(len(ds)):
          # find better way to get target
          # targets = ds.get_annotations(img_idx)
          img, targets = ds[img_idx]
          image_id = targets["image_id"]
          img_dict = {}
          img_dict["id"] = image_id
          img_dict["height"] = img.shape[-2]
          img_dict["width"] = img.shape[-1]
          dataset["images"].append(img_dict)
          bboxes = targets["boxes"].clone()
          bboxes[:, 2:] -= bboxes[:, :2]
          bboxes = bboxes.tolist()
          labels = targets["labels"].tolist()
          areas = targets["area"].tolist()
          iscrowd = targets["iscrowd"].tolist()
          if "masks" in targets:
              masks = targets["masks"]
              # make masks Fortran contiguous for coco_mask
              masks = (
                  masks.permute(0, 2, 1).contiguous().permute(0, 2, 1)
              )
          if "keypoints" in targets:
              keypoints = targets["keypoints"]
              keypoints = keypoints.reshape(
                  keypoints.shape[0], -1
              ).tolist()
          num_objs = len(bboxes)
          for i in range(num_objs):
              ann = {}
              ann["image_id"] = image_id
              ann["bbox"] = bboxes[i]
              ann["category_id"] = labels[i]
              categories.add(labels[i])
              ann["area"] = areas[i]
              ann["iscrowd"] = iscrowd[i]
              ann["id"] = ann_id
              if "masks" in targets:
                  ann["segmentation"] = coco_mask.encode(
                      masks[i].numpy()
                  )
              if "keypoints" in targets:
                  ann["keypoints"] = keypoints[i]
                  ann["num_keypoints"] = sum(
                      k != 0 for k in keypoints[i][2::3]
                  )
              dataset["annotations"].append(ann)
              ann_id += 1
      dataset["categories"] = [{"id": i} for i in sorted(categories)]
      coco_ds.dataset = dataset
      coco_ds.createIndex()
      return coco_ds
  
file_path: vision/references/detection/coco_utils.py
imports: import os
  import torch
  import torch.utils.data
  import torchvision
  import transforms
  from pycocotools import mask
  from pycocotools.coco import COCO
label_desc: Use image node_id 3 for calling a global function with example usage: convert_to_coco_api(ds) and returns: coco_ds